dse is a simple and crude way of not executing SQL queries in sequence, but caching values until a given max value has been met and then execute them using the executemany-method. The result can be huge speed gains.dse was only tested on SQLite3 but intended for use in django as well.Example usage:import sqlite3 # for testing purposesfrom dse import DelayedSqlExecutorconn = sqlite3.connect(\':memory:\')cursor = conn.cursor()cursor.execute(\'create table filedata (id INTEGER PRIMARY KEY, filepath TEXT, filename TEXT, filesize INTEGER)\')d = DelayedSqlExecutor(cursor, paramtoken= \'?\') # using the ? paramtoken here for sqlite3. Leave it blank and it`ll use %s as support by Django etc.d.addObject(\'filedata\', (\'id\', \'filepath\', \'filename\', \'filesize\'))for i in range(0, 999): # adding some dummy data. Notice the absence of the id-field. This will trigger inserts. #Adding the id-field would trigger an update for data not yet in the db d.addItem(\'filedata\', {\'filepath\': \'/tmp/\', \'filename\': \'test%s.txt\' % i, \'filesize\': i})# No SQL has been executed yet, the default limit is 1000 items# Adding another item will trigger the execution of SQLs and reset the d-instanced.addItem(\'filedata\', {\'filepath\': \'/tmp/\', \'filename\': \'test%s.txt\' % i, \'filesize\': i})# Adding some records to updated.addItem(\'filedata\', {\'id\': 1, \'filepath\': \'/tmp/\', \'filename\': \'testmore%s.txt\' % i, \'filesize\': 100})# calling close will execute any remaining SQLsd.close()# you might be required to call commit on the cursor to commit the data. Depends on how you set up the cursor/connection.